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Hello.
My name is Martin Kronberg, and
this is the IoT Developer Show

season two.
During our break, we've been
busy reworking the show,

so think of this less
like a sequel and more

like the gritty reboot.
We'll be coming
out with a new show

every other Wednesday for
the rest of the season.

Moving forward, the
IoT Dev Show is going

to have an all new format.
We're going to be taking deep
dive looks into specific IoT

technologies over the course
of multiple episodes grouped

into a series.
Last season, I gave you guys
a broad overview of all the

cool Intel IoT tech with
some special guests.

And this season I'll be
up here, a one man show,

leading you through deeper dives
into the technology, the tools

available for developers,
and demos that have

been built using those tools.
For the first
series of episodes,

we're taking a look at Open
Visual Inference and Neural

Network Optimization
Toolkit, or more

simply, the OpenVINO Toolkit,
which gives developers

the power to create cutting
edge AI powered computer vision

applications.
Intel computer
vision technologies

have grown over the
last year and have

combined with Intel's
Deep Learning Toolkit

to form OpenVINO.
But before we get to
the details of OpenVINO,

let me show you
guys a cool demo.

Here is the head position and
emotional state detector demo.

It's running on a
brand new IEI Tank,

which is a coupe
piece of hardware

that we're going to
be covering later on.

I'm using a couple of
deep neural network models

to detect the position
and orientation

of my face, an
analysis of my gender,

my age, and even my mood.
All this is running at
the edge on the tank

and running at over
120 frames per second.

And that's what
OpenVINO's all about--

leveraging powerful neural
network processing of video

as fast as possible
on Intel architecture.

Want to learn more about
how this demo works

and how you can build
something like this yourself?

Well, stay tuned, because
we're going to cover

all of that and much more.
First of all, let's
do a quick overview

of traditional computer
vision versus deep learning.

In traditional computer
vision, an image

is analyzed using
programmatic methods.

For instance, if we're
looking to identify a face,

one method uses Haar
cascade classifiers.

This method relies on taking
the difference of pixel values

in various areas and linking
it to known features,

such as edges, eyes, so on.
We can then say that two
eyes and an oval is a face.

In deep neural
networks, this approach

is radically different.
Instead of telling the computer
of what features to look for--

eyes and so on--
we show the computer
10,000 images

of a face from various
angles, and then it

learns what it looks
like by adjusting

the structure of a
complex, interconnected

network of nodes.
If this sounds like a black box
to you, you wouldn't be alone.

In an article from the
MIT Technology Review

called The Dark Secret
at the Heart of AI,

AI engineer Joel Dudley said,
"We can build these models,

but we don't know
how they work."

But the fact of the matter
is that they do work and work

extremely well.
In fact, with purpose
built deep learning models,

a computer can recognize objects
faster and more accurately

than any human.
But for now, what
we need to know

is that deep learning has two
components-- a training phase,

where the computer learns
to identify objects,

and an inference phase,
where the now trained

model is used to infer the
identity of unknown objects.

Now, with that out of the
way, let's take a look

at what's inside OpenVINO.
It's a combination of tools
for computer vision and AI.

It uses OpenCV 3.3,
which has been optimized

for Intel architecture.
OpenCV can be used
for pre-processing

an image for analysis
and then running analysis

on it, either through the
traditional programmatic

methods or deep neural networks.
OpenVINO also has a custom
inference engine built by Intel

for running deep neural
networks for computer vision.

And inference engine
is what's used

to run the inference
phase of deep learning

that I mentioned earlier.
What makes this
inference engine awesome

is its flexibility
and its performance.

It's made to utilize
both your Intel

CPU, your integrated Intel
GPU, as well as a VPU,

like the Movidius Compute
Stick, or an FPGA,

like the Altera Arria 10.
It's also been optimized to
use the latest and fastest APIs

to access all of
those processors.

Using various processors
for a single task

is called heterogeneous
computing,

and it's part of what
makes OpenVINO so fast.

So how can you start
developing using this toolkit?

Well, we have a ton
of documentation out

there on IDZ and a few GitHub
pages to get you started.

We also have two
developer kits--

the UP Squared AI
vision Development

Kit that can be used
for rapid prototyping,

and the IEI Tank,
which can be used

for more demanding applications
in an industrial environment.

They both come loaded
with all the software

alongside awesome
hardware to help you

get started developing fast.
That's all the time
we have for today.

In the next four
episodes, we're going

to cover all we saw
today in more detail.

I'm going to show you
more awesome demos,

talk about all the neural
net models available,

the IDEs that you can
use, and deep dive

into some of the
reference designs.

We're also going
look at the hardware

and talk about
heterogeneous computing.

Thanks for watching, and we'll
see you guys in two weeks.

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Introducing OpenVINO and Computer Vision | IoT Developer Show Season 2 | Intel Software

57 Folder Collection
alex published on April 26, 2019
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